I have a vector of monthly log asset price returns. I would like use the boot 
package to sample one-year returns, and then calculate confidence intervals on 
the loss distribution only. More concretely, I would like to say something like 
"99% of LOSSES (not RETURNS) are above cutoff X."

If the variable "x" contains my returns and I was interested in confidence 
intervals for the entire distribution of returns, I might do something like 
this:

results <- boot(x, statistic=function(d, indx) sum(d[indx[1:12]]), R=100000)
boot.ci(results, conf=.98)

In this case I establish a cutoff for the bottom 1% of the return distribution, 
but this is not the same thing as the bottom 1% for the losses only. It seems 
one thing to do would be to just extract the sample returns from the results 
object, filter them, and then calculate confidence intervals manually. However, 
I was curious if there was a simple way to subset the results object so that it 
could be fed directly into boot.ci()

Thanks.

Abiel
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